Hedging for Privacy in Smart Water Meters

Smart meters for enhanced water supply network modelling and infrastructure planning

This article deals with the use of machine learning to detect sudden water leakage. A smart water meter, which enables monitoring the water consumption of the observed object, is used as the source of input data. Based on these data and their analysis, a symbolic regression, which must know not only the input parameters but also the structure of the model, was finally used to build the model. After finding a suitable function and standard deviation from the model, it is possible to set the required sensitivity and thereby detect anomalous states of water consumption in monitored time windows. Since the smart water meter also has a ball valve, if a sudden water leakage is detected, the water meter can autonomously close the main supply and thus avoid extensive damage.

The urge to manage daily energy consumption has now become a worldwide priority. The Internet of Things (IoT)-based smart meter has been widely used to monitor energy consumption in many households. LoRaWAN connectivity is confirmed to be one of the most reliable connectivity to be used as a network system for IoT use cases. It can fulfill the needs of low power, long-range, efficient, and affordable IoT technology in utility energy monitoring. LoRaWAN-based smart electric meters are designed in rural areas for monitoring and managing electric energy consumption. The smart electric meter system consists of smart kWh meters, LoRa gateway, network system, and dashboard for energy usage monitoring. The system design process consists of feature design, architecture design, data flow design, LoRa gateway site planning, and platform setup. The implementation can be concluded that LoRa can be used for smart electric meters in rural areas. A gateway can reach a maximum distance of 1.58 km, with an RSSI of -99.40 dBm ±4.56 dBm. LoRa gateway availability can reach 100% by implementing gateway protection systems. The LoRaWAN-based smart electricity meter has also proven to be costoptimized, efficiently reducing monthly costs to 92.12%.

This paper reviews on some of the recent methodologies of intelligent water metering system used in the smart water meter (SWM) to overcome the problem of water crises. The conservation of water is one of the vital problems in worldwide that needs proper attention. Due to the unpredictable rise in water consumption, the entire fresh water sources are diminishing day by day. At present, there is a gradual advancement in the technologies used for the purpose of monitoring of the proper utilization of water sources. This resulted into better functioning and solving various water loss issues in broad spectrum. The different frameworks available for SWM including the advanced communication protocols are also being discussed in this paper that could help for further advancement in the SWM.KeywordsSmart water meter (SWM)FrameworksProtocolsIntelligent water metering system

The water leakage detection on pipelines has improved with the advent of remote environmental wireless sensor network (WSN) and Internet of Things (IoT) technologies over the past few years. Leakage detection in underground water pipelines involves algorithms to predict leakage and estimate its location, in which accuracy depends significantly on the distance between the water meters stationed at district water stations. This research proposes to deploy smart wireless water meter nodes at intermediate distances to enhance the accuracy sharply. This requires water meters to operate autonomously, harvesting energy from surroundings, to be self-sufficient. This research proposes a heterogeneous communication with smart water meters that focuses on transferring data and reducing energy consumption by establishing communication with wireless sensor nodes for smart water meter applications.

Water monitoring in households provides occupants and utilities with key information to support water conservation and efficiency in the residential sector. High costs, intrusiveness, and practical complexity limit appliance-level monitoring via sub-meters on every water-consuming end use in households. Non-intrusive machine learning methods have emerged as promising techniques to analyze observed data collected by a single meter at the inlet of the house and estimate the disaggregated contribution of each water end use. While fine temporal resolution data allow for more accurate end-use disaggregation, there is an inevitable increase in the amount of data that needs to be stored and analyzed. To explore this tradeoff and advance previous studies based on synthetic data, we first collected 1 s resolution indoor water use data from a residential single-point smart water metering system installed at a four-person household, as well as ground-truth end-use labels based on a water diary recorded over a 4-week study period. Second, we trained a supervised machine learning model (random forest classifier) to classify six water end-use categories across different temporal resolutions and two different model calibration scenarios. Finally, we evaluated the results based on three different performance metrics (micro, weighted, and macro F1 scores). Our findings show that data collected at 1- to 5-s intervals allow for better end-use classification (weighted F-score higher than 0.85), particularly for toilet events; however, certain water end uses (e.g., shower and washing machine events) can still be predicted with acceptable accuracy even at coarser resolutions, up to 1 min, provided that these end-use categories are well represented in the training dataset. Overall, our study provides insights for further water sustainability research and widespread deployment of smart water meters.

Urban water conservation through customised water and end-use information

The evolution of urban water metering and conservation in Australia

An autonomous and intelligent expert system for residential water end-use classification

The use of smart meters have been increased in many countries across the globe. Smart meters are not only used for measurement of electricity consumption but also used for the measurement of gas and water consumption. The smart meter is an integral part of the smart grid system and its use incurs benefits to people in various aspects such as social, economic and environmental. The smart meters' main functionality is measuring, capturing and transforming data related to usage or consumption of electricity, gas or water and events such as meter status and power quality. Such functionality encounters many data related issues such as the volume of data, the speed of collecting data and complexity. To gain the hidden insights and knowledge from such data we require the advanced data analytical tools and technologies. This paper provides a brief review of tools and technologies used in smart electricity meter data analytics as well as a comprehensive survey of several key analytical applications and techniques and tools used in those applications.

Water management, metering and billing are essential services for any industry and residential settlement, and having these running as efficiently as possible is key to an improved standard of living. Energy efficiency and reading or topping up meters remotely are important in ensuring no loss to the regulatory authority or disruption of service to the user. Smart water meters have emerged as a major innovation in the drive to achieve this efficiency through multiple forms and protocols that have shown varying energy consumption, range and durability, relieving physical water meter readers from the strains of doing their job in inclement weather, difficult terrain or under poor coordination. To model and develop a system that addresses these issues as closely as possible to Zimbabwe’s current needs, the study assesses real-world systems deployed in Harare, taking into account the costs, logistics, licensing and other challenges involved in current operation. This study focuses on the energy analysis and development of a remote smart meter system using LoRaWAN as a communication protocol. This compact system aims to be as energy conscious and efficient as possible, all while flawlessly collecting meter data and transmitting it to a remote gateway or billing system. It focuses on the energy consumption of the system when used by different users models on their consumption, recharge behavior and system activity up-time.

Flow time series decomposition to identify non-revenue water components in drinking water distribution systems: A data-driven approach.

Smart Water Meters (SWM) hold significant potential to mitigate water scarcity challenges in tourism-intensive destinations, yet their effectiveness in non-residential contexts remains underexplored. This study evaluates the impact of digital metering and feedback mechanisms on water consumption within tourism accommodations, utilising a geolocated panel dataset encompassing 213 high water-consuming establishments in San Bartolomé de Tirajana (Canary Islands). As a leading year-round destination, this region faces operational constraints that complicate conservation initiatives. Our comprehensive dataset integrates water network technical characteristics, tourism demand and supply variables, and climate factors across sub-municipal tourism zones to account for diverse guest profiles and management practices. Employing a random-effects regression model, we identify key determinants of water usage and uncover distinct consumption patterns between hotels and apartment complexes. The results reveal that while SWM installation significantly improves consumption tracking and enables early leak detection—primarily benefiting utilities—access to frequent digital feedback via web platforms correlates with increased water use. These findings challenge the assumption that digitalisation alone ensures efficiency, emphasising the importance of behavioural, organisational, and financial strategies while offering actionable insights for policymakers, utilities, and tourism operators to optimise digital engagement and adapt water governance.

Smart electric meters collect data on electricity use to potentially improve efficiency for utilities, shape power consumption, and ultimately reduce greenhouse gas emissions. Consumer groups and experts have raised concerns about privacy, as smart meters can provide detailed information about activity within the home. This study examines privacy beliefs of U.S. consumers using qualitative data from focus groups together with experimental data from a national online survey of utility customers. Exploratory analysis of focus group findings suggested that consumers who felt in control of their data perceived fewer risks and more benefits from having access to the data. Participants in the online experiment saw greater risk and less benefit when advised that smart meters could be used to raise prices under time‐of‐use pricing. Participants also saw greater risk when the utility shared customer data with a third party. Risks and benefits influenced perceptions of control. These findings accord with several information privacy frameworks adding new insights about consumers' perceived control over their energy data. The findings can inform utility companies and policymakers with respect to giving consumers more control over their data, reducing consumers' privacy concerns, communicating benefits of smart meter data, and providing accurate information about the risks.

Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review